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arXiv 1701.03066math.PRmath-phmath.APmath.COmath.MP
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空间分数阶随机偏微分方程正则结构的模型空间

Model Spaces of Regularity Structures for Space-Fractional SPDEs

Nils Berglund, Christian Kuehn

摘要:

我们研究模型空间,在Hairer的意义下,随机偏微分方程涉及分数拉普拉斯算子。我们证明了分数拉普拉斯算子是一个奇异核,适用于正则结构理论。我们的主要贡献是研究的依赖模型空间的正则结构上的三个参数的问题,涉及的空间维度,多项式阶的非线性,和指数的分数拉普拉斯算子。我们的目标是研究参数变化下模型空间的增长。特别是,我们证明了几个结果在接近次临界极限导致普遍增长指数的规律性结构。一个关键的作用是由这样的观点,即模型空间可以确定与家庭的有根树。我们的证明是基于一个几何结构类似于牛顿多边形的经典泰勒级数和各种组合参数。我们还提出了几个明确的例子,列出了所有元素的负同质性,通过实施一个新的符号软件包与正则结构。我们用这个包来说明我们的分析结果,并获得新的approachtures关于模型空间的粗粒度网络措施。

英文摘要:

We study model spaces, in the sense of Hairer, for stochastic partial differential equations involving the fractional Laplacian. We prove that the fractional Laplacian is a singular kernel suitable to apply the theory of regularity structures. Our main contribution is to study the dependence of the model space for a regularity structure on the three-parameter problem involving the spatial dimension, the polynomial order of the nonlinearity, and the exponent of the fractional Laplacian. The goal is to investigate the growth of the model space under parameter variation. In particular, we prove several results in the approaching subcriticality limit leading to universal growth exponents of the regularity structure. A key role is played by the viewpoint that model spaces can be identified with families of rooted trees. Our proofs are based upon a geometrical construction similar to Newton polygons for classical Taylor series and various combinatorial arguments. We also present several explicit examples listing all elements with negative homogeneity by implementing a new symbolic software package to work with regularity structures. We use this package to illustrate our analytical results and to obtain new conjectures regarding coarse-grained network measures for model spaces.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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